System and method for generating an ocular dysfunction nourishment program

Abstract

A system and method for generating an ocular dysfunction profile outcome is presented. The system comprising a computing device configured to determine an ocular assessment as a function of receiving an ocular attribute datum, generate an ocular profile as a function of the ocular assessment, identify at least an edible as a function of the ocular profile, a nourishment composition, and an edible classifier, and develop a profile outcome including a treatment outcome and a prevention outcome as a function of the edible.

What is claimed is: 
     
         1 . A system for generating an ocular dysfunction nourishment program, the system comprising a computing device configured to:
 determine an ocular assessment as a function of receiving an ocular attribute datum;   generate an ocular profile as a function of the ocular assessment, wherein the generation includes:
 determine at least an ocular vector as a function of the ocular attribute datum; and 
 generate a degree of variance as a function of the ocular vector and an ocular utopia; 
   identify at least an edible as a function of the ocular profile, a nourishment composition, and an edible classifier, wherein the identification includes:
 determining an ocular dysfunction as a function of the ocular profile using a dysfunction training set correlating at least an ocular enumeration and a visual system effect to the ocular dysfunction; and 
   develop a profile outcome including a treatment outcome and a prevention outcome as a function of the edible.   
     
     
         2 . The system of  claim 1 , wherein the ocular assessment includes an applanation tonometry. 
     
     
         3 . The system of  claim 1 , wherein the ocular profile includes a visual health status. 
     
     
         4 . The system of  claim 3 , wherein the ocular utopia represents an ideal visual health status. 
     
     
         5 . The system of  claim 1 , wherein the computing device is configured to generate a degree of variance as a function of the ocular vector and the ocular utopia. 
     
     
         6 . The system of  claim 1 , wherein the identifying at least an edible includes:
 training an edible machine-learning model by training data that contains nourishment compositions and ocular profiles as inputs correlated to a plurality of edibles as outputs; and   outputting the edible as a function of training the edible machine-learning model.   
     
     
         7 . The system of  claim 1 , wherein the computing device generates the edible classifier using a K-nearest neighbors (KNN) algorithm. 
     
     
         8 . The system of  claim 1 , wherein the computing device identifies at least an edible as a function of a likelihood parameter and a user taste profile. 
     
     
         9 . The system of  claim 1 , wherein the at least an edible contains one or more flavor variables. 
     
     
         10 . The system of  claim 1 , wherein the computing device is further configured to develop a nourishment program as a function of the profile outcome and the at least an edible. 
     
     
         11 . A method for generating an ocular dysfunction nourishment program, the method comprising:
 determining, by a computing device, an ocular assessment as a function of receiving an ocular attribute datum;   generating, by the computing device, an ocular profile as a function of the ocular assessment, wherein the generation includes:
 determining at least an ocular vector as a function of the ocular attribute datum; and 
 generating a degree of variance as a function of the ocular vector and an ocular utopia; 
   identifying, by the computing device, at least an edible as a function of the ocular profile, a nourishment composition, and an edible classifier, wherein the identification includes:
 determining an ocular dysfunction as a function of the ocular profile using a dysfunction training set correlating at least an ocular enumeration and a visual system effect to the ocular dysfunction; and 
   developing, by the computing device, a profile outcome including a treatment outcome and a prevention outcome as a function of the edible.   
     
     
         12 . The method of  claim 11 , wherein the ocular assessment includes an applanation tonometry. 
     
     
         13 . The method of  claim 11 , wherein the ocular profile includes a visual health status. 
     
     
         14 . The method of  claim 13 , wherein the ocular utopia represents an ideal visual health status. 
     
     
         15 . The method of  claim 11 , wherein the computing device is configured to generate a degree of variance as a function of the ocular vector and the ocular utopia. 
     
     
         16 . The method of  claim 11 , wherein the identifying at least an edible includes:
 training an edible machine-learning model by training data that contains nourishment compositions and ocular profiles as inputs correlated to a plurality of edibles as outputs; and   outputting the edible as a function of training the edible machine-learning model.   
     
     
         17 . The method of  claim 11 , wherein the computing device generates the edible classifier using a K-nearest neighbors (KNN) algorithm. 
     
     
         18 . The method of  claim 11 , wherein the computing device identifies at least an edible as a function of a likelihood parameter and a user taste profile. 
     
     
         19 . The method of  claim 11 , wherein the at least an edible contains one or more flavor variables. 
     
     
         20 . The method of  claim 11 , wherein the computing device is further configured to develop a nourishment program as a function of the profile outcome and the at least an edible.